The Role and Function of the Liaison Officer: Lessons Learned and Applied after Superstorm Sandy
Bibliographic record
Abstract
In October 2012, Superstorm Sandy had a wide impact on the public across New York City (NYC). The NYC Department of Health and Mental Hygiene (DOHMH) activated its incident command system (ICS) and deployed a liaison officer (LNO) to the NYC Emergency Operations Center (EOC) at NYC Emergency Management (NYCEM) 24 hours a day for 6 weeks. This prolonged response period, coupled with environmental effects on NYC's coastal communities, increased public awareness of Sandy's health impacts, requiring a broad scope of interagency coordination and operational input from the liaison officer. Liaison officers involved in this response later conducted a content analysis of issues handled throughout Sandy, to better understand the skill set required to serve in this role, identify greater staff depth, integrate liaison officers into DOHMH exercises, and update just-in-time training provided before liaison officers deploy. This analysis revealed defined training topics for liaison officers to improve staff performance and effectiveness in leading interagency coordination during emergency responses. Topics include resources, staffing, data management, public messaging, and vulnerable populations, and these topics have since been used to revamp liaison officer training and guide policy changes in the liaison officer job charter. Targeted use of liaison officers to support development and implementation and to coordinate response objectives with local, state, and federal partners has only become more important. This analysis continues to influence how DOHMH defines its citywide agency response role, to inform how best to staff and train liaison officers to respond, and to pose lessons for other jurisdictions seeking to maximize the effectiveness of liaison officers deployed in emergencies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".